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models/loader.py
Centralized lazy model loader.
Models are loaded once on first use and cached in memory.
"""
import torch
import whisper
from transformers import (
RobertaTokenizer, RobertaModel,
ViTImageProcessor, ViTModel,
pipeline as hf_pipeline
)
_cache = {}
def get_device():
return 'cuda' if torch.cuda.is_available() else 'cpu'
# ── RoBERTa ──────────────────────────────────────────────
def get_roberta():
if 'roberta' not in _cache:
print("[loader] Loading RoBERTa-large...")
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
model = RobertaModel.from_pretrained('roberta-large').to(get_device())
model.eval()
_cache['roberta'] = (tokenizer, model)
return _cache['roberta']
# ── ViT ──────────────────────────────────────────────────
def get_vit():
if 'vit' not in _cache:
print("[loader] Loading ViT-L/16...")
extractor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224')
model = ViTModel.from_pretrained('google/vit-large-patch16-224').to(get_device())
model.eval()
_cache['vit'] = (extractor, model)
return _cache['vit']
# ── Whisper ───────────────────────────────────────────────
def get_whisper():
if 'whisper' not in _cache:
print("[loader] Loading Whisper base...")
_cache['whisper'] = whisper.load_model('base')
return _cache['whisper']
# ── Sentiment pipeline ────────────────────────────────────
def get_sentiment():
if 'sentiment' not in _cache:
print("[loader] Loading sentiment pipeline...")
_cache['sentiment'] = hf_pipeline(
'sentiment-analysis',
model='cardiffnlp/twitter-roberta-base-sentiment-latest',
device=0 if get_device() == 'cuda' else -1
)
return _cache['sentiment']
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